About
Data Scientist. Platform Engineer.
Applied mathematician turned ML infrastructure builder. Pipelines that scale, systems that ship.
Hi, I'm Walter.
I'm a father, husband, and lifelong tinkerer who fell into data science through an honest love of applied mathematics. My academic work focused on Population Dynamics and Epidemiology, specifically using early warning signal theory to predict and suppress epidemic extinction events. That paper is now published, and yes, I still find it deeply strange that my career started with mathematical biology.
Professionally, I work at the intersection of data science, ML engineering, and AI-native developer tooling. I've built forecasting pipelines, ML platforms, feature stores, and more recently agentic workflows powered by large language models. The throughline in all of it: I care more about whether a system actually gets used and trusted than whether it's technically elegant. Both ideally, but trust wins.
When I'm not behind a keyboard, you'll find me running trails, chasing my daughter around, planning the next international trip, or deep in a bookstore. According to my friends, if you were to lose me inside one, you'd most likely find me in the "World Domination" section. Outside of that: competitive practical shooter (IPSC/USPSA/PCSL) and marathon runner.
Career
The Journey So Far
2013 – 2016
B.S. Mathematics, Montclair State University
Top Graduating Senior. Researched Population Dynamics and Epidemiology, developing an early warning signal framework to predict and suppress epidemic extinction events (later published in the International Journal of Chaos and Dynamics).
2017 – 2022
Data Scientist, EZOPS
Built ML models for client behavior analysis and record deduplication at scale. Grew into ML platform work including experiment tracking, feature engineering, and model monitoring across financial services data.
2022 – 2024
Data Science Product Manager, EZOPS
Led product strategy for the data science platform. Owned the roadmap, ran discovery cycles, and drove alignment between engineering and business stakeholders across the DS product surface.
2024 – Present
Staff Data Scientist, Polly.io
Building ML platform infrastructure, applied AI systems, and agentic developer tooling at a fintech scale. Work spans a five-module loan volume forecasting ML framework (FACTS through MONITORING), a Bayesian pull-through rate analysis engine, a production AI analytics application on Databricks Apps, a three-tier LLM classification system, and a six-figure infrastructure cost reduction program — alongside the DLT streaming modernization and knowledge graph that underpins it all.